Abstract:
Managing the capacity of delivery lockers are disclosed herein. For example, by utilizing machine learning techniques, the capacity management system described herein may determine portions of a delivery locker to reserve for packages delivered at one or more delivery speeds. For example, the system may train one or more machine learning models to determine factors associated with a delivery demand, dwell time probability, and optimized capacity reservation of the delivery locker. Utilizing these factors, the system may determine a portion of the delivery locker to reserve for packages delivered at each available delivery speed.
Abstract:
Systems and methods for authenticating a request submitted from a client device through a third party content provider to an electronic entity are described. In one embodiment, a method includes providing a trusted script to the third party content provider, passing a trust token to the third party content provider and to the client device, and, in response to a request submitted from the client device through the third party content provider, validating the trust token associated with the request with the token passed to the client device, and processing the request. The trusted script is configured to create a trusted window on the third party Web page displayed on the client computing device, receive a trust token from the electronic entity through the trusted window, and associate the trust token with requests submitted from the client computing device through the third party content provider to the electronic entity.
Abstract:
Technologies are provided for generation of recommendation results using a verbal query. In one embodiment, query data can be generated using a searchable query corresponding to the verbal query. The query data can define a query browse node and a product brand, for example. First product identifiers that match the searchable query can be determined using multiple data repositories. Duplicates from the first product identifiers can be removed, resulting in second product identifiers. Attribute data also can be generated using the second product identifiers. The attribute data can define features for a product identifier. Further, third product identifiers can be determined by applying a filtering model to the second product identifiers. A ranking of the third product identifiers can be generated using an optimization function based on the query data and the product attribute data. A product corresponding to one of the ranked product identifiers represents a recommendation result.
Abstract:
Dialog management may be performed by a voice assistant system where the dialog pertains to a shopping experience that enables a user to order one or more items using voice-activated commands provided during a dialog exchange with the voice assistant device. In some embodiments, the personal assistant system may enable a user to order items, select fulfillment details for the items, pay for the items, and/or perform other related tasks to enable the user to obtain the items using voice activated commands and without reliance on a graphical user interface. In various embodiments, the personal assistant system may select fulfillment options for a user, or may assign fulfillment to a particular service based on audio responses received from a user. The personal assistant system may leverage prior user interaction data, user profile information, and/or other user information during interaction with a user to supplement voice inputs received from a user.